About the Role
We're seeking a Senior AI Architect to design enterprise-scale AI/ML platforms that bridge business needs with production reality. You'll define how models are deployed, monitored, and scaled—focusing on architecture, reliability, and operationalization rather than model development itself. This role sits at the intersection of AI strategy, platform engineering, and technical leadership—ensuring AI capabilities scale efficiently for the products.
Key Responsibilities
- AI Platform Architecture & Design: Design end-to-end AI platform covering data pipelines, feature engineering, model training, deployment, serving, and monitoring. Define reference architectures for key use cases: real-time inference, batch predictions, recommendation systems, NLP/CV pipelines. Make technology stack decisions balancing model accuracy, latency, cost, and maintainability. Design cloud-native, scalable architectures using Kubernetes, microservices, and event-driven patterns.
- Production ML & Model Serving: Architect high-throughput, low-latency model serving infrastructure with HA requirements. Establish deployment patterns: A/B testing, canary releases, shadow deployments, rollback strategies. Guide model optimization: quantization, pruning, distillation, ONNX/TensorRT compilation. Design comprehensive model validation frameworks with offline/online metrics and quality gates.
- MLOps & Platform Engineering: Build end-to-end MLOps: automated pipelines, model versioning, artifact management, CI/CD. Design model registries with metadata management, promotion workflows (dev → staging → prod). Architect feature stores ensuring consistency between training/serving and low-latency access. Establish standards for reproducibility: environment management, data versioning, experiment tracking.
- Observability & Production Support: Design ML observability: track latency, throughput, prediction distribution, feature/output drift. Implement automated alerting for accuracy drops, latency spikes, data quality issues. Build stakeholder dashboards showing model performance, system health, and business impact.
- Technical Leadership & Strategy: Conduct design reviews, provide architectural guidance, make final technical decisions. Mentor data scientists and engineers on production best practices and system design. Design and Code Review AL/ML Model and Algorithm code. Define platform standards: coding practices, documentation, architectural decision records. Evaluate emerging technologies (LLMs, RAG, multimodal models) and assess organizational fit. Partner with product, engineering, and compliance teams on AI governance and strategy.
Required Qualifications
Experience
- 12+ years in software engineering with 6+ years in AI/ML architecture and production deployment.
- Proven track record architecting ML platforms serving enterprise-scale production workloads.
- Strong foundation in both data science principles and production engineering practices.
Core Technical Skills
- Python expertise: ML workflows, PyTorch/TensorFlow, scikit-learn, pandas, FastAPI/Flask.
- Container orchestration: Kubernetes, Docker, Helm; model serving: TorchServe, Triton, Seldon, KServe.
- MLOps tools: MLflow, Kubeflow, Airflow, DVC, W&B (or equivalent platforms).
- Distributed systems: microservices, event-driven architecture, REST/gRPC APIs, Kafka, Redis.
- Data infrastructure: PostgreSQL/MongoDB, StarRocks, feature stores (Feast/Tecton).
- Cloud platforms: Deep expertise in AWS/GCP/Azure (compute, storage, networking, ML services).
- Observability: Prometheus, Grafana, Loki stack, cloud-native monitoring solutions.
AI Architecture Knowledge
- Strong grasp of ML fundamentals, model lifecycle, evaluation metrics—sufficient to guide architectural decisions.
- Deep understanding of production ML patterns: batch/real-time inference, streaming ML, feature stores.
- Model optimization techniques: quantization, pruning, distillation, ONNX/TensorRT compilation.
- LLM deployment patterns: RAG architectures, vector databases, fine-tuning strategies (LoRA), cost-performance trade-offs.
- Experience with deep learning infrastructure: distributed training, GPU clusters, large model deployment.
Leadership & Soft Skills
- Systems thinking: ability to see the big picture, understand interdependencies, design holistic solutions.
- Communication: articulate complex technical concepts to engineers, data scientists, product managers, executives.
- Decision-making: comfortable with architectural decisions under uncertainty, balancing trade-offs.
Nice to Have
- Experience in regulated industries (healthcare, finance) with compliance requirements.
- Edge ML, on-device inference, federated learning experience.
- Domain expertise in computer vision, NLP, time-series, or recommendation systems.
- Master's/PhD in Computer Science, ML, or related field (or equivalent experience).
What We Offer
- High-impact role shaping technical direction and platform strategy.
- Opportunity to design next-generation AI infrastructure from the ground up.
- Collaborative environment with talented engineers, data scientists, and product leaders.
- Competitive compensation: base salary, equity, comprehensive benefits.
- Professional development support: conferences, training, certifications.